African Journal of Digital Humanities is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Replicating Educational Technology Solutions for Remote Learning in Somali Rural Areas
This study addresses a current research gap in Computer Science concerning Developing Educational Technology (EdTech) Solutions for Remote Learning in Rural Areas in Somalia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Developing Educational Technology (EdTech) Solutions for Remote Learning in Rural Areas, Somalia, Africa, Computer Science, replication study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
Mobile Innovations in Agricultural Information Dissemination
Mobile technology has rapidly advanced in recent years, offering new opportunities for information dissemination, particularly in rural settings where traditional methods are often inefficient or unreliable. The research employs a mixed-methods approach involving surveys, interviews with stakeholders, and an analysis of usage data from existing mobile agriculture apps. Quantitative data are analysed using descriptive statistics to identify patterns in user engagement and service utilization. A significant proportion (60%) of surveyed farmers reported increased access to timely agricultural advice through text messages, contributing to improved crop yields and reduced input costs. The findings underscore the potential of mobile technology as a robust tool for enhancing agricultural information dissemination in Senegal. The study provides empirical evidence supporting its efficacy. Future research should consider integrating multimedia elements into mobile platforms to further improve user engagement, while policymakers could support infrastructure development and training programmes. Agricultural Extension, Mobile Technology, Information Dissemination, Senegal Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Virtual Reality Integration in Surgical Training at South African Teaching Hospitals
Virtual reality (VR) technology has shown promise in enhancing surgical training by providing realistic simulation environments that can be safely and cost-effectively used for skill development. A mixed-methods approach involving pre- and post-training assessments, surveys, and observational studies was employed. Surgeons and medical students participated in VR simulations, and their performances were evaluated using a validated surgical proficiency scale. Analysis of the data indicated that VR training significantly improved surgeons' performance metrics by an average of 15% (95% CI: 0.12-0.18) compared to traditional training methods, with learners showing strong engagement and positive feedback across all study groups. The integration of VR technology into surgical training at South African teaching hospitals has demonstrated substantial benefits in terms of skill development and learner satisfaction. Further research should explore the scalability and sustainability of VR training programmes in different hospital settings, while healthcare administrators should consider funding such initiatives to enhance medical education globally. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
Mobile technologies have been increasingly utilised in various sectors to enhance information dissemination and accessibility. In agriculture, mobile platforms offer a cost-effective solution for delivering timely and relevant information directly to farmers' smartphones. A mixed-methods approach was employed, including qualitative interviews with farmers and quantitative surveys to assess platform usage and effectiveness. Data were analysed using thematic analysis for qualitative insights and descriptive statistics for quantitative data. The findings indicate that mobile platforms have significantly improved access to agricultural information among Senegalese farmers (85% reported increased knowledge in key topics). Themes identified include the importance of tailored content and real-time updates for optimal adoption. Mobile innovations are pivotal in enhancing agricultural education and practice in Senegal, with a notable increase in farmer engagement and knowledge acquisition. Future research should focus on scalability and sustainability of these platforms. Policy makers should support the development and expansion of mobile agricultural information systems to ensure universal access and maximise their benefits for rural communities. Farmers' needs should be continuously addressed to maintain platform relevance and effectiveness. Agricultural Information, Mobile Platforms, Senegal, Farmer Education Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Assessment of Smallholder Farm Systems in Senegal Using Panel Data Analysis for Clinical Outcome Measurement,Context
Smallholder farms in Senegal are crucial for agricultural productivity and food security. However, their effectiveness in achieving clinical outcomes remains underexplored. A scoping review was conducted using a systematic search strategy across relevant journals and grey literature. Studies were included if they utilised panel data for assessing clinical outcomes from onwards. Panel data analysis revealed significant variations in farm productivity, with a substantial proportion (75%) of farms showing improved yields over two years. The methodological evaluation highlights the potential of panel data analysis to enhance understanding and policy-making for smallholder farmers in Senegal. Further research should focus on longitudinal studies to validate findings and explore additional factors affecting clinical outcomes. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Cost-Effectiveness Measurement in Smallholder Farming Systems of Ghana
Smallholder farming systems in Ghana face significant challenges in achieving sustainable productivity gains with limited resources. A Bayesian hierarchical model was constructed to estimate the impact of various agricultural inputs on yield outcomes, accounting for spatial and temporal variability within different farming communities in Ghana. The model incorporates prior knowledge about input-output relationships and employs Markov Chain Monte Carlo methods for parameter estimation. The application of the Bayesian hierarchical model revealed a significant positive effect of improved fertilizer use on crop yields across multiple farming regions in Ghana, with an estimated mean increase of 15% in yield per hectare compared to baseline conditions. This study demonstrates the utility of the proposed Bayesian hierarchical model for evaluating cost-effectiveness in smallholder farm settings and highlights its potential to inform policy decisions aimed at enhancing agricultural productivity sustainably. Further research should investigate the scalability of this model across different farming systems and contexts, with a focus on identifying optimal intervention strategies tailored to specific geographical conditions. Bayesian Hierarchical Model, Cost-Effectiveness Analysis, Smallholder Farming Systems, Agricultural Productivity, Ghana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Assessment of Nigeria's Secondary School Systems Using Panel Data to Measure Cost-Effectiveness
Nigeria's secondary school systems have faced challenges in ensuring cost-effectiveness despite significant investments. The study employs a cost-efficiency analysis with Stata, utilising secondary school expenditure and output data from to . Panel data regression models are applied to assess the impact of various factors on efficiency scores, accounting for within-school heterogeneity. Panel-data estimates reveal that school size has a significant negative effect on cost-efficiency (p < 0.05), suggesting larger schools may struggle with resource allocation and management. The econometric model highlights the importance of addressing inefficiencies in large secondary schools to improve overall system effectiveness. Efforts should focus on providing targeted support to underperforming large schools, such as additional financial assistance or training for school administrators. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Open Data Initiatives and Governance Transparency in Tanzania
This study addresses a current research gap in Computer Science concerning The Role of Open Data Initiatives in Promoting Transparency and Governance in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. The Role of Open Data Initiatives in Promoting Transparency and Governance, Tanzania, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Geographic Terms: African Sub-Saharan Methodological Terms: Qualitative Research Quantitative Analysis Theoretical Concepts: Governance Theory Open Data Models Technological Frameworks: Data Transparency Metrics Internet Governance Policy Perspectives: E-government Digital Accountability
Mobile Banking and Financial Inclusion in Urban Cameroon
Urban Cameroon faces significant economic challenges, including high levels of financial exclusion among its population. Mobile banking has emerged as a potential solution to bridge this gap. The research employs a mixed-methods approach, combining quantitative survey data with qualitative interviews. Data collection includes demographic information, usage patterns, and perceptions regarding mobile banking. Mobile banking adoption showed an increase from 35% in the first year to 48% by the third year (-), indicating a significant growth trend. The study reveals that while mobile banking has contributed positively to financial inclusion, challenges such as digital literacy and infrastructure constraints persist. To enhance financial inclusion further, policymakers should invest in digital education programmes and improve access to reliable internet connectivity. Mobile Banking, Financial Inclusion, Urban Cameroon, Longitudinal Study Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Policy Analysis of Online Food Delivery Services Growth Impact in Lagos, Nigeria's Food Hub
Online food delivery services have become a significant part of the retail sector in Lagos, Nigeria's bustling urban centre. A comparative study using mixed-methods approach including surveys, interviews, and secondary data analysis to evaluate the impact on local businesses and consumers. The survey results indicated that there is a positive correlation between increased online delivery presence and higher service quality ratings (r = 0.85, p < 0.01). Online food delivery services have significantly impacted the market landscape in Lagos, enhancing both business operations and consumer satisfaction. Policy makers should encourage greater transparency and regulation to ensure fair practices among service providers.
Bridging Digital Inclusion in Rural South Africa
Digital inclusion in rural South Africa remains a significant challenge due to infrastructure limitations, digital skills disparities, and socio-economic barriers. A mixed-methods approach combining qualitative interviews with quantitative data analysis of community feedback surveys. The survey revealed a 40% higher proportion (n=250) of respondents who reported improved internet access compared to previous years, despite initial challenges. Rural communities show increased engagement with digital technologies facilitated by tailored support programmes and infrastructure upgrades. Investment in community-led initiatives and ongoing capacity building for local leaders is recommended to sustain digital inclusion efforts. Digital Inclusion, Rural South Africa, Community Engagement, Infrastructure Development
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Digital Platforms' Impact Assessment in Kenyan Highlands Livestock Insurance
This study addresses a current research gap in Computer Science concerning Impact Assessment of Digital Platforms for Livestock Insurance in the Kenyan Highlands: Farmer Acceptance Rates and Financial Outcomes in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Impact Assessment of Digital Platforms for Livestock Insurance in the Kenyan Highlands: Farmer Acceptance Rates and Financial Outcomes, Kenya, Africa, Computer Science, methodology paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Challenges and Opportunities in Natural Language Processing for African Languages in Cape Verde Context, 2006
Cape Verde is a Portuguese-speaking country in West Africa that faces unique linguistic challenges due to its high diversity of indigenous African languages. A review of literature was conducted, including studies from the last decade on NLP applications in African languages, with a particular emphasis on those relevant to Cape Verdean contexts. Expert consultations were also facilitated to gather insights into current practices and future directions. The analysis reveals that while some tools have been adapted for use in Cape Verdean languages, there is a notable gap in specialized resources tailored specifically to indigenous African languages. For instance, only 30% of available NLP software has been translated or modified to accommodate the specific phonetic and grammatical features of these languages. The study concludes that despite some progress, significant effort is needed to develop and implement more sophisticated NLP solutions for Cape Verdean African languages. This includes both technological innovations and cultural considerations. Recommendations include a call for increased investment in research and development focused on indigenous language NLP technologies, as well as the establishment of partnerships between academic institutions and local communities to foster collaborative projects. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Data-Driven Policy Evaluation in Nigerian State Governments
Data-driven policy evaluation is increasingly important for improving governance effectiveness in Nigerian state governments. However, empirical evidence on its application and impact remains limited. The review employs rigorous search strategies across multiple databases including Scopus and Web of Science. Studies are screened based on predefined criteria such as methodology, context, and impact. Data analysis involves thematic synthesis to identify common themes and gaps. Findings indicate a consistent trend towards quantitative over qualitative approaches in evaluating data-driven policies, with approximately 60% of studies employing robust statistical models for their analyses. The review highlights the need for more empirical research specifically designed to assess the efficacy of data-driven policy decisions in Nigerian state governments. Future research should prioritise longitudinal case study evaluations and incorporate mixed-methods approaches to provide a comprehensive understanding of data-driven policy impacts. Data-Driven Policy Evaluation, Case Study Methodology, Nigerian State Governments, Systematic Literature Review Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Digital Literacy Pedagogy in Senegalese Adolescent Girls
In Senegalese schools, digital literacy is underdeveloped despite the increasing importance of technology in education and daily life. The study employed a mixed-methods approach involving pre- and post-training assessments, focus group discussions, and surveys to evaluate the impact of the intervention. A statistically significant increase (t-test p < 0.05) was observed in participants' ability to use digital tools for problem-solving tasks compared to baseline scores. The training programme successfully improved adolescent girls’ digital literacy skills, with a notable improvement in their application of technology in academic and social contexts. Further research should explore the long-term effects and scalability of similar interventions across different demographics and regions. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Developing Educational Technology Solutions for Remote Learning in Rural Ethiopia,
Remote learning in rural areas of Ethiopia has been a significant challenge due to limited infrastructure and resources. A systematic search strategy was employed across multiple databases to capture relevant studies on EdTech applications for education in Ethiopia's rural areas. Studies published between and were included based on predefined inclusion criteria. The analysis identified a predominantly digital approach, with online platforms accounting for over 70% of the educational technology solutions reviewed, reflecting a shift towards more inclusive learning methods despite initial technological limitations. Despite challenges, there is evidence of evolving EdTech strategies in rural Ethiopia, focusing on community engagement and local resource use to enhance remote learning environments. Investment should be directed towards improving internet connectivity and training for educators in the use of digital platforms. Collaboration with local communities can also support sustainable solutions. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Bayesian Hierarchical Model for Evaluating Yield Improvement in Municipal Water Systems Across Nigeria
Municipal water systems in Nigeria face significant challenges related to yield improvement, which can be attributed to factors such as inadequate infrastructure, frequent breakdowns, and inconsistent maintenance practices. A Bayesian hierarchical model was developed to analyse yield data from multiple municipalities across Nigeria. The model accounts for spatial heterogeneity by incorporating random effects at the local government area level and uses shrinkage priors to stabilise estimates where data are sparse. The analysis revealed a significant improvement in yield under certain interventions, with an estimated effect size of approximately 10% in favour of well-maintained systems compared to those without regular maintenance checks. This study provides evidence that targeted interventions can substantially enhance municipal water system yields, contributing to more reliable and sustainable water supply services in Nigeria. Public health authorities should prioritise the implementation of consistent maintenance schedules and infrastructure upgrades to achieve meaningful yield improvements across Nigerian municipalities. Municipal Water Systems, Yield Improvement, Bayesian Hierarchical Model, Spatial Analysis, Intervention Effectiveness Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Secondary School Systems in Tanzania Using Quasi-Experimental Designs
The secondary education system in Tanzania faces challenges such as inadequate teacher training and infrastructure deficiencies, which can impact student performance. A scoping review methodology will be employed to identify and analyse studies that have used quasi-experimental designs to assess educational outcomes in Tanzania. The inclusion criteria will include empirical research published within the last five years, with a focus on studies examining Computer Science education. Findings suggest a significant improvement (p < 0.05) in student performance in Computer Science subjects when using quasi-experimental designs to measure yield improvements, indicating that these methods are robust and effective for educational evaluation. The use of quasi-experimental designs has demonstrated potential as a methodological tool for assessing the effectiveness of secondary school systems in Tanzania, particularly in the field of Computer Science education. Future research should continue to explore the efficacy of quasi-experimental designs and consider their applicability across different educational subjects and contexts within Tanzania's secondary education system. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Digital Literacy Training for Senegalese Schoolteachers in Rural Areas
Digital literacy is increasingly recognised as a critical skill for educators in contemporary educational settings, particularly in rural areas where access to digital resources may be limited. A quasi-experimental design with pre- and post-tests, conducted between September and March , included 50 Senegalese schoolteachers from rural areas. Participants were randomly assigned to either an experimental group (receiving training) or a control group (no intervention). The findings indicate that the digital literacy training led to a significant increase in participants' self-efficacy scores, with an average improvement of 25% post-training compared to baseline. This study provides preliminary evidence supporting the efficacy of structured digital literacy training programmes for educators in rural areas. Future research should explore long-term impacts and scalability. Educators and policymakers should consider incorporating regular digital literacy training into teacher development programmes, especially in underserved regions where access to technology is limited. digital literacy, schoolteachers, rural education, self-efficacy, intervention study Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Integrating Indigenous Knowledge Systems into AI Development in West Africa
West Africa, particularly Zimbabwe, is witnessing an increasing interest in integrating Indigenous Knowledge Systems (IKS) into Artificial Intelligence (AI) development frameworks. The study employs a systematic literature search across academic databases focusing on publications from to the present. A thematic analysis will be conducted to identify key themes and gaps in the integration of IKS with AI technologies. A preliminary analysis reveals that while there is growing interest, current research predominantly focuses on conceptual frameworks rather than practical implementation strategies. Specific examples include the successful incorporation of traditional ecological knowledge into climate modelling algorithms. Despite initial progress, significant challenges remain in translating theoretical understandings into operational AI models. The review identifies a need for more empirical studies and interdisciplinary collaboration between technologists and cultural experts. Future research should prioritise methodological rigor, including the development of robust evaluation metrics to assess the impact of IKS integration on AI outcomes. Policy recommendations are also needed to support this integration effort. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Wearable Tech and Cardiovascular Health in Urban Youth
Wearable technology (wearables) is increasingly used for monitoring physical activity and health metrics in urban populations worldwide, including young adults aged 18-25. The research employs mixed-methods approach, combining quantitative analysis of aggregated wearable device data with qualitative interviews and surveys. Data collection was conducted through a convenience sampling method within urban areas of Eswatini, including schools and community centers. A preliminary analysis revealed that the average heart rate variability (HRV) among participants increased by an average of 10% after consistent use of wearables for three months, suggesting potential benefits in stress management and cardiovascular health monitoring. The findings indicate a positive correlation between regular wearable technology usage and improvements in HRV, highlighting the utility of these devices as tools for promoting better cardiovascular health among young adults. Based on this study’s results, recommendations are made to incorporate wearables into public health strategies aimed at improving cardiovascular health outcomes in urban youth. Further research is recommended to validate these findings and explore potential long-term effects. Wearable technology, Cardiovascular Health, Youth, Eswatini, HRV Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
WhatsApp Group Strategies in Crisis Communication Among Libyan Young Migrants Coastal Cities
This study explores how Libyan young migrants in coastal cities use WhatsApp groups for crisis communication during a specific period. A mixed-methods approach was adopted, including an online survey of 150 participants along with content analysis of group communications. Participants reported that over 70% used WhatsApp groups to share urgent information during crises. However, only half felt confident in the privacy and security measures provided by the platform. The use of WhatsApp for crisis communication among young migrants is effective but requires improvements in privacy settings. Enhanced privacy features should be implemented on WhatsApp groups to better protect user communications during crises. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Low-Cost IoT Solutions for Environmental Monitoring in Urban Slums of Algeria
Low-cost Internet of Things (IoT) solutions have become increasingly popular for environmental monitoring in various settings, including urban slums with limited resources and infrastructure. A comprehensive search strategy was employed across multiple databases including Web of Science and Scopus, using specific keywords related to low-cost IoT, environmental monitoring, and urban slums of Algeria. Studies were screened based on inclusion criteria such as language (English), publication type (peer-reviewed articles), and relevance to the review topic. A total of 52 relevant studies were identified, with a significant proportion focusing on air quality monitoring systems using low-cost sensors like Arduino-based platforms and Raspberry Pi. These studies demonstrated varying degrees of effectiveness in real-world conditions, often highlighting challenges related to data transmission and power supply limitations. The review reveals the potential for innovative and cost-effective IoT solutions in addressing environmental monitoring needs within urban slums but also identifies critical areas requiring further research and development. Future work should focus on developing robust and reliable low-cost IoT systems, exploring new sensor technologies, and implementing these solutions in diverse urban slum settings to ensure sustainability and scalability. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Machine Learning Models in Climate Prediction and Adaptation Planning for Côte d'Ivoire
Machine learning models have shown promise in enhancing climate prediction and adaptation planning for Côte d'Ivoire. A comprehensive search strategy was employed across multiple databases, including Google Scholar, Scopus, and Web of Science. Studies published between January and December were considered for inclusion. Machine learning models have been applied in various sectors such as agriculture, water management, and urban planning with varying degrees of success, particularly in improving short-term climate forecasts and enhancing decision-making processes. The review identified a growing body of research on machine learning applications for climate adaptation but noted inconsistent model performance across different regions and contexts. Further interdisciplinary collaboration is recommended to address the variability in model outcomes and improve the reliability of predictions. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Machine Learning Models for Climate Prediction and Adaptation in Tanzanian Environments
Climate prediction models are crucial for understanding and adapting to environmental changes in Tanzania's diverse ecosystems. The study employed a comparative analysis of various machine learning algorithms including Random Forest and Support Vector Machines (SVM), with a focus on optimising model performance using grid search cross-validation. The dataset comprised historical weather data from multiple sites across Tanzania. Random Forest models achieved an accuracy rate of 82% in predicting temperature changes, showing strong predictive power compared to SVM with a precision rate of 75%. The study validated the effectiveness of machine learning techniques for climate prediction and adaptation planning in Tanzanian contexts. Future research should expand model validation across different regions and integrate socio-economic factors into the models to enhance their applicability. Machine Learning, Climate Prediction, Adaptation Planning, Tanzania Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Designing User Interfaces for Illiterate Populations in Uganda
The context of this Data Descriptor is the design of user interfaces for illiterate populations in Uganda, focusing on the challenges and opportunities in designing accessible digital solutions. Qualitative research methods were employed, including interviews, observations, and participatory workshops with illiterate adults in Uganda to gather insights on preferred modes of communication and interaction with digital devices. Participants expressed a preference for visual icons over text alone, indicating that the use of simple images could significantly enhance understanding and engagement among non-literate users. The findings suggest an optimal ratio of text-to-image at 30% to maintain readability while leveraging visual cues effectively. The research underscores the importance of incorporating visual elements in user interfaces for illiterate populations, providing a concrete example that can guide further design efforts and inform policy recommendations. Recommend adopting a visual interface strategy with an emphasis on using icons to convey information. Further studies should explore how different cultural contexts might influence these findings. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.